{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_"
      },
      "source": [
        "# Quick start"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "on5cvb0VoK_D"
      },
      "source": [
        "Welcome to the quick start guide for AlphaGenome! The goal of this tutorial\n",
        "notebook is to quickly get you started with using the model and making\n",
        "predictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNBquB49Nk6W"
      },
      "source": [
        "```{tip}\n",
        "Open this tutorial in Google Colab for interactive viewing.\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "W28j15ApoK_D",
        "language": "python",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827023069,
          "user_tz": 0,
          "elapsed": 1,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "# @title Install AlphaGenome\n",
        "\n",
        "# @markdown Run this cell to install AlphaGenome.\n",
        "from IPython.display import clear_output\n",
        "! pip install alphagenome\n",
        "clear_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lcTVQLvqB6F"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KuLPy_awXaP_",
        "language": "python",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827027354,
          "user_tz": 0,
          "elapsed": 4031,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "from alphagenome import colab_utils\n",
        "from alphagenome.data import gene_annotation\n",
        "from alphagenome.data import genome\n",
        "from alphagenome.data import transcript as transcript_utils\n",
        "from alphagenome.interpretation import ism\n",
        "from alphagenome.models import dna_client\n",
        "from alphagenome.models import variant_scorers\n",
        "from alphagenome.visualization import plot_components\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jEGP__L4oK_E"
      },
      "source": [
        "## Predict outputs for a DNA sequence"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bzov6O0goK_F"
      },
      "source": [
        "AlphaGenome is a model that makes predictions from DNA sequences. Let's load it\n",
        "up:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThpPPG1A8L-D"
      },
      "source": [
        "```{tip}\n",
        "If using Google Colab, store your key in \"Secrets\" for persistent access across sessions (see [installation](https://www.alphagenomedocs.com/installation.html#google-colab)). Otherwise, `dna_client.create` can take the API key directly.\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "toBpbthy93gr",
        "language": "python",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827027717,
          "user_tz": 0,
          "elapsed": 111,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "dna_model = dna_client.create(colab_utils.get_api_key())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_358E_nkYVt"
      },
      "source": [
        "The model can make predictions for the following\n",
        "[output types](https://www.alphagenomedocs.com/exploring_model_metadata.html):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "executionInfo": {
          "elapsed": 3,
          "status": "ok",
          "timestamp": 1761827027975,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "U2Q-amDjkYVt",
        "outputId": "2246354e-20e1-44c7-c5a9-56c12f071de7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ATAC',\n",
              " 'CAGE',\n",
              " 'DNASE',\n",
              " 'RNA_SEQ',\n",
              " 'CHIP_HISTONE',\n",
              " 'CHIP_TF',\n",
              " 'SPLICE_SITES',\n",
              " 'SPLICE_SITE_USAGE',\n",
              " 'SPLICE_JUNCTIONS',\n",
              " 'CONTACT_MAPS',\n",
              " 'PROCAP']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "[output.name for output in dna_client.OutputType]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dpFi65J3kYVt"
      },
      "source": [
        "AlphaGenome predicts multiple 'tracks' per output type, covering a wide variety\n",
        "of tissues and cell-types. However, predictions can be made efficiently for\n",
        "subsets of interest.\n",
        "\n",
        "Here is how to make DNase-seq predictions (as specified by `OutputType`) in a\n",
        "subset of tracks corresponding to lung tissue (as specified by `ontology_terms`)\n",
        "for a DNA sequence of length 1Mb:\n",
        "\n",
        "*Note: We use ontology terms from standardized biological sources like UBERON\n",
        "(for anatomy) and the Cell Ontology (CL) to provide consistent and widely\n",
        "recognized classifications for tissue and cell types.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "pRv5MT9hkYVt",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827029303,
          "user_tz": 0,
          "elapsed": 1052,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "output = dna_model.predict_sequence(\n",
        "    sequence='GATTACA'.center(\n",
        "        dna_client.SEQUENCE_LENGTH_1MB, 'N'\n",
        "    ),  # Pad to valid sequence length.\n",
        "    requested_outputs=[dna_client.OutputType.DNASE],\n",
        "    ontology_terms=['UBERON:0002048'],  # Lung.\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHz9nXh9kYVt"
      },
      "source": [
        "The `output` object contains predictions for all the different requested output\n",
        "types (in this case, only output type `DNASE`). Predictions for genomic tracks\n",
        "are stored inside a `TrackData` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "executionInfo": {
          "elapsed": 1,
          "status": "ok",
          "timestamp": 1761827029605,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "KmQMwe1MkYVt",
        "outputId": "e3d2cfc3-ae1d-48ab-95ce-3ba269afa544"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alphagenome.data.track_data.TrackData"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dnase = output.dnase\n",
        "type(dnase)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HflwfcSxkYVt"
      },
      "source": [
        "`TrackData` objects have the following components:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pudb_vcWkYVt"
      },
      "source": [
        "<a href=\"https://services.google.com/fh/files/misc/trackdata.png\"><img src=\"https://services.google.com/fh/files/misc/trackdata.png\" alt=\"trackdata\" border=\"0\" height=500></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0R24IDvokYVt"
      },
      "source": [
        "The predictions of shape `(sequence_length, num_tracks)` are stored in\n",
        "`.values`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1761827029950,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "7OoINJM_kYVt",
        "outputId": "c046bca4-fb7b-4d90-bb44-fafb5337d562"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1048576, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00187683],\n",
              "       [0.00177765],\n",
              "       [0.00177765],\n",
              "       ...,\n",
              "       [0.0019989 ],\n",
              "       [0.00292969],\n",
              "       [0.0039978 ]], shape=(1048576, 1), dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "print(dnase.values.shape)\n",
        "\n",
        "dnase.values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kAs7jA_kYVt"
      },
      "source": [
        "And the corresponding metadata describing each of the tracks is stored in\n",
        "`.metadata`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "height": 115
        },
        "executionInfo": {
          "elapsed": 104,
          "status": "ok",
          "timestamp": 1761827030304,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "6SPQ6zhukYVt",
        "outputId": "1729ef0c-7920-4ab2-b034-40c608a537f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"dnase\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"UBERON:0002048 DNase-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"strand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \".\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Assay title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"DNase-seq\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ontology_curie\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"UBERON:0002048\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"lung\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"tissue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_life_stage\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"embryonic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"data_source\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"encode\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"endedness\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"paired\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genetically_modified\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonzero_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.4275049567222595,\n        \"max\": 0.4275049567222595,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.4275049567222595\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-076ee16b-0db1-44af-a8c4-bda20d45ba5a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>strand</th>\n",
              "      <th>Assay title</th>\n",
              "      <th>ontology_curie</th>\n",
              "      <th>biosample_name</th>\n",
              "      <th>biosample_type</th>\n",
              "      <th>biosample_life_stage</th>\n",
              "      <th>data_source</th>\n",
              "      <th>endedness</th>\n",
              "      <th>genetically_modified</th>\n",
              "      <th>nonzero_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>UBERON:0002048 DNase-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>DNase-seq</td>\n",
              "      <td>UBERON:0002048</td>\n",
              "      <td>lung</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.427505</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-076ee16b-0db1-44af-a8c4-bda20d45ba5a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-076ee16b-0db1-44af-a8c4-bda20d45ba5a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-076ee16b-0db1-44af-a8c4-bda20d45ba5a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                       name strand  ... genetically_modified nonzero_mean\n",
              "0  UBERON:0002048 DNase-seq      .  ...                False     0.427505\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dnase.metadata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KLVSBMxVkYVt"
      },
      "source": [
        "In this case, there is only one output track, so the track metadata returns only\n",
        "1 row.\n",
        "\n",
        "The track metadata is especially useful when requesting predictions for multiple\n",
        "tissues or cell-types, and when dealing with stranded assays (which are assays\n",
        "with separate readouts for the two DNA strands, such as CAGE and RNA-seq):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "executionInfo": {
          "elapsed": 965,
          "status": "ok",
          "timestamp": 1761827031557,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "LGYCvBbmkYVt",
        "outputId": "6187e567-44eb-48bc-f2ae-762aa1c800fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DNASE predictions shape: (1048576, 2)\n",
            "CAGE predictions shape: (1048576, 4)\n"
          ]
        }
      ],
      "source": [
        "output = dna_model.predict_sequence(\n",
        "    sequence='GATTACA'.center(\n",
        "        dna_client.SEQUENCE_LENGTH_1MB, 'N'\n",
        "    ),  # Pad to valid sequence length.\n",
        "    requested_outputs=[\n",
        "        dna_client.OutputType.CAGE,\n",
        "        dna_client.OutputType.DNASE,\n",
        "    ],\n",
        "    ontology_terms=[\n",
        "        'UBERON:0002048',  # Lung.\n",
        "        'UBERON:0000955',  # Brain.\n",
        "    ],\n",
        ")\n",
        "\n",
        "print(f'DNASE predictions shape: {output.dnase.values.shape}')\n",
        "print(f'CAGE predictions shape: {output.cage.values.shape}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sA4plQHMkYVt"
      },
      "source": [
        "Notice that in this example, we requested predictions for 2 assays and 2\n",
        "ontology terms simultaneously.\n",
        "\n",
        "The CAGE track metadata describes the strand and tissue of each of the 4\n",
        "predicted tracks (2 per DNA strand):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "height": 175
        },
        "executionInfo": {
          "elapsed": 3,
          "status": "ok",
          "timestamp": 1761827031880,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "89vnUT2KkYVt",
        "outputId": "a9f0cdb4-7978-4a23-8e1f-ff0065c044c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"output\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"hCAGE UBERON:0002048\",\n          \"hCAGE UBERON:0000955\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"strand\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"-\",\n          \"+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Assay title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"hCAGE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ontology_curie\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"UBERON:0002048\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"lung\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"biosample_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"tissue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"data_source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"fantom\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nonzero_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.283800687168554,\n        \"min\": 28.4322452545166,\n        \"max\": 30.655853271484375,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          30.655853271484375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>strand</th>\n",
              "      <th>Assay title</th>\n",
              "      <th>ontology_curie</th>\n",
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              "      <th>0</th>\n",
              "      <td>hCAGE UBERON:0000955</td>\n",
              "      <td>+</td>\n",
              "      <td>hCAGE</td>\n",
              "      <td>UBERON:0000955</td>\n",
              "      <td>brain</td>\n",
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              "      <td>fantom</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hCAGE UBERON:0002048</td>\n",
              "      <td>+</td>\n",
              "      <td>hCAGE</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "        async function quickchart(key) {\n",
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              "            const charts = await google.colab.kernel.invokeFunction(\n",
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              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   name strand  ... data_source nonzero_mean\n",
              "0  hCAGE UBERON:0000955      +  ...      fantom    28.432245\n",
              "1  hCAGE UBERON:0002048      +  ...      fantom    30.655853\n",
              "2  hCAGE UBERON:0000955      -  ...      fantom    28.432245\n",
              "3  hCAGE UBERON:0002048      -  ...      fantom    30.655853\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "output.cage.metadata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HemMKknzkYVt"
      },
      "source": [
        "See the\n",
        "[output metadata documentation](https://www.alphagenomedocs.com/exploring_model_metadata.html)\n",
        "for more information on the output types and output shapes. For the mapping\n",
        "between tissue names (e.g. 'brain' -> 'UBERON:0000955') and ontology terms, see\n",
        "this [tutorial](tissue_ontology_mapping.ipynb)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rL6BQQfqkYVt"
      },
      "source": [
        "## Predict outputs for a genome interval (reference genome)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZuGzeStkYVt"
      },
      "source": [
        "For convenience, you can also directly make predictions for a human reference\n",
        "genome sequence specified by a **genomic interval**. For example, let's predict\n",
        "RNA-seq for tissue 'Right liver lobe' in a 1MB region of Chromosome 19 around\n",
        "the gene *CYP2B6*, which encodes an enzyme involved in drug metabolism, and is\n",
        "primarily expressed in the liver.\n",
        "\n",
        "We first load up a GTF file containing gene and transcript locations as\n",
        "annotated by GENCODE (more information on GTF format\n",
        "[here](https://www.gencodegenes.org/pages/data_format.html)):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "1WE7BBh6klIA",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827039153,
          "user_tz": 0,
          "elapsed": 6988,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "# The GTF file contains information on the location of all trancripts.\n",
        "# Note that we use genome assembly hg38 for human.\n",
        "gtf = pd.read_feather(\n",
        "    'https://storage.googleapis.com/alphagenome/reference/gencode/'\n",
        "    'hg38/gencode.v46.annotation.gtf.gz.feather'\n",
        ")\n",
        "\n",
        "# Set up transcript extractors using the information in the GTF file.\n",
        "# Mane select transcripts consists of of one curated transcript per locus.\n",
        "gtf_transcripts = gene_annotation.filter_protein_coding(gtf)\n",
        "gtf_transcripts = gene_annotation.filter_to_mane_select_transcript(gtf_transcripts)\n",
        "transcript_extractor = transcript_utils.TranscriptExtractor(gtf_transcripts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5PjqsMCkYVt"
      },
      "source": [
        "And then fetch the gene's location as a `genome.Interval` object by passing\n",
        "either its `gene_symbol` (HGNC naming convention) or ENSEMBL `gene_id`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "executionInfo": {
          "elapsed": 94,
          "status": "ok",
          "timestamp": 1761827039765,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "pngLCscokYVt",
        "outputId": "01faa17e-74ac-4e11-d00c-51c565ad8f54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Interval(chromosome='chr19', start=40991281, end=41018398, strand='+', name='CYP2B6')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "interval = gene_annotation.get_gene_interval(gtf, gene_symbol='CYP2B6')\n",
        "interval"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6fLrfgyvkYVt"
      },
      "source": [
        "We can resize it to a length compatible with the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "VV5k05MykYVt",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827040053,
          "user_tz": 0,
          "elapsed": 52,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "interval = interval.resize(dna_client.SEQUENCE_LENGTH_1MB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pu1A32KJkYVt"
      },
      "source": [
        "The `.resize()` method adjusts the interval to the specified width by expanding\n",
        "(or contracting) around its original center. Note that\n",
        "`dna_model.predict_interval()` interprets this resizing as an expansion of the\n",
        "actual genomic sequence rather than padding tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "executionInfo": {
          "elapsed": 3,
          "status": "ok",
          "timestamp": 1761827040345,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "3TdgcNRskYVt",
        "outputId": "8ffb0177-0884-4d6a-afe9-3dfe2c873e60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1048576"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "interval.width"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hI5hi6DckYVu"
      },
      "source": [
        "See the\n",
        "[essential commands documentation](https://www.alphagenomedocs.com/colabs/essential_commands.html)\n",
        "for more handy commands like `resize`.\n",
        "\n",
        "Note that AlphaGenome supports the following input sequence lengths:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "executionInfo": {
          "elapsed": 2,
          "status": "ok",
          "timestamp": 1761827040632,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "22gZecZTkYVu",
        "outputId": "e0e44f51-daa6-4776-bf3f-18a836ca5464"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['SEQUENCE_LENGTH_16KB', 'SEQUENCE_LENGTH_100KB', 'SEQUENCE_LENGTH_500KB', 'SEQUENCE_LENGTH_1MB'])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "dna_client.SUPPORTED_SEQUENCE_LENGTHS.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRk3W7yjkYVu"
      },
      "source": [
        "We can now make predictions using our interval:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "executionInfo": {
          "elapsed": 934,
          "status": "ok",
          "timestamp": 1761827041851,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "ME7NH0B-kYVu",
        "outputId": "8d1b9aa0-71e5-422e-d486-2115727afba4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1048576, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "output = dna_model.predict_interval(\n",
        "    interval=interval,\n",
        "    requested_outputs=[dna_client.OutputType.RNA_SEQ],\n",
        "    ontology_terms=['UBERON:0001114'],\n",
        ")  # Right liver lobe.\n",
        "\n",
        "output.rna_seq.values.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZD6iGfrkYVu"
      },
      "source": [
        "In general, you can have multiple tracks for a given ontology term. In this\n",
        "case, we have 3 RNA-seq tracks for the tissue \"Right liver lobe\".\n",
        "\n",
        "Let's visualise these predictions. It's helpful visualise gene transcripts\n",
        "alongside the predicted tracks, so we extract them here:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "executionInfo": {
          "elapsed": 244,
          "status": "ok",
          "timestamp": 1761827042347,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "zU61GXrekYVu",
        "outputId": "e1f0ec0b-2ae9-459d-975f-14b7908a4760"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracted 28 transcripts in this interval.\n"
          ]
        }
      ],
      "source": [
        "transcripts = transcript_extractor.extract(interval)\n",
        "print(f'Extracted {len(transcripts)} transcripts in this interval.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hUwvkl_kYVu"
      },
      "source": [
        "We also provide a\n",
        "[visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html)\n",
        "that integrates nicely with `TrackData` and other objects returned by the model\n",
        "API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "height": 300
        },
        "executionInfo": {
          "elapsed": 2735,
          "status": "ok",
          "timestamp": 1761827045351,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "MjJsLGnGkYVu",
        "outputId": "85d1cbe1-fa05-4436-ee4c-dc818d0173ca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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onBI5lFUVbftJIN93CAkBhdO8wm/x9lexPCq3JVesTubRfy1JTiiplCmetM1MWTY35zgAAADay9KGGk1O+Zwyv3Zpxgt42+qYd27S/ROH6rfv3qqYx/XwxLd00aeP687xb+jurwbl1Vef6h5tiiW2Ct9NZtaKhfrLh/9puWErzKld3Pj3reNeK8o8VjTUqSbHC/M6m4Z4TIvqlpc6jIIYOGGIdn7lPO38ynlNzu24u/Z94xKdMWKgzhxxf9Y+xixqn8q76e5Gds+Etl0QCiA3VaUOAAAAAEDHFve4KqxCt459tcnwX79zk4b+eEBpglpFxD2uXV+9oPH56zM/03N7n60vFk3TlGVz9cuNdlOv6m4ljBDlIPmK/RHzv5a7h+poKJg5NYs1asEk7bnWVupe1aXJuMX1K/SjQZc1GbbjGpu0Z3ittvtaW2jGivn6eMGktONX1bTHp6e8r/smvKlHf/hX9aoK+8hpy+fpF8OulSR9t8+3dd9up7R5Pr9//86c2yYSgh/Y7TRt1WeDjO0Gz/y8zXF1Vg0F+tGxSL/PtjtuIYrmwmeDKqtsPibD54bWJixs13djfbJwsiSpS2V1q/pAc82SZ4u4wypEJeh93rhEv9ts37TjGuIxfbpwitbr3lfvz/2qcbiZNfls+/miqW2OAwAAIJPEeWtJmrF8gcYunq7d19pSXSurtbh+hW4b96pWq+quU7c4oPEzc/J5oPO/+wsdtuFO+u3wWzQmurX9+wdeUdDzcqkVfBfXL9eNY19ufP7o5OH629aH5NxfWyNblS/ou3/iUL0/b3zR55OaBDlm0XR1q6xWv15rN2s7fsk3um70izpr60O12WrrpO2vPt6gY4ffoolLZ0uSbtzxBO221haFD3wVNXrRdB3/7q2SpNO3/In2XWdbbdBjjRJH1Tr18QbdNn5lwvj1Y17UP7Y5TJL01dKZjcNb+g1g97W20IszRjY+L9bvBXWx9BebJ86DDj/gMlVVND8HAaDtqPALAAAAoCAmLp2tI4Zdq+emfSQpnPw7f9Qj2u+NS3Xr2Fd1/8ShTdrnepvYzqAu3qDPFk7RwpSr0O9Jc3vcw4b+W+eNekR3jH9d//j4ofYKEauQXV49X6/MGNVseMzjRas0Us4a4jF9uWha1h8k3po9WrNWLEw7rj7eoJ8O+ZfO/+RR7f3GgGbj/5amosKyhvT7t56VXXOKuTX2XGurvKepsorGW7ynk8v2UoptqiEe0zNTP9DcmsXNxr0/d7z+9eVzml27WPu9cWnj8ESyr1S85KF06+KbFQuaVP/9bfQDRCZ1WaqyHrz+Dq0ProMr7A+OHWM/WUfCL1JURD/upf7YtvMr5+mF6PN7qsenvNt8YJp9Xer+r1+vtRr/jq/CCQHlJnU9F3Nvle02zvm4b8Kbzfv2uHZ/7UL96YO7ddjQf+vKL55pHFch05eLClOxHQAAdF4N8ZiuG/0/HTf8Fk2KkiST1cUbtPMr52nXVy/QXePf0IqGOh03/GadM+q/umXcK5Kk44ffqqenfqD7Jw7V8e/e1jjtP0auPB97xedPS1Jjsq+kjBdVt1Zycq8kLa2vbVN/bf2c11DCz/c3jHlJPx50uV6ZMaqxAmo+3zeenPJ+EaNL75FJ7+i3796qX759g16a/nGTce6u37xzs0bM/1rHvHNjxj7u/3poY7KvJJ0xYmCxwi177t7sjkbHJ51ru2nsy/r5sGs0bPZoSdKEJbP0syFX6/rRL7Z6nkvra3Tc8Fv0+/fuLPrF1U9Mea/J8+TfiypSzuGmq66bST4XyVenuUg4k9oW7i715w/va/I85vHG96y768tF0zSvdknO8wOwEhV+AQAAAORsYd1ynTXyAR210W46cP3tmoz71ds3SAonOrfru5F+GT2X1CzZFys98PWwxhPJkjT0RwO0sH6ZBn49VM8kVbdK56P5Xxc7PKyiLvr0cR20/vaNzycsmaUT37tN3+q6mu7b9RT17dKz2TSfLZyi+ya8qT3W2lJHbrRrO0abXXKy5LsHXp53xbndX7uw8e8PDroya/937Hyy+q+xaZPxZ3w0MGv/X6RJSqlKirFLRVVjEmcxfxRZq1vv/CdqobJDLj8B1cUb1LWdqzcmXtN/Kuwzk6su/+Wj4tyaMZ2TNttX9yYlMtXGG9QtaV3Uxxt02NB/59Vnz6rMVdsLUW2xo9otqRp+W3WMdF+p3rP/6ILOJ7HHT7cvWdJQk3aaa0f/r9mwdO+R1MqzyfNYlSuAlZtCJeHmNK8ivm7JCTOpvqlZqK4VVS3+cAwAAJDN23PG6tHJwyVJV3zxjO7e5Y9Nxl/+2dONf98zYbDW6d638TPxY5Pf1d+3PlTTV8xvbDNm8fTGvycvn1vM0JuZmXKBepfKtqX4xNt44XYpPt+7u3Z59fzG5xd9+njj37u+ekHa833l4voxKxNNB3z2hA7eYOXF3AvrlzVpO2np7LRVgO/6alDxAlwFLKlfof1T7q522fd/1ez3qWRnjXxQHxx0ZWMi9SOT39F3+35b+66zre6b8KZmrFig8777c3WpaPn9dOqH92hslNR/2edP65LvH9WGpcnuo3lNf+/pU92j8e/Ud24sy3s59Tt6Qzyu6hxPK9Z7LOeKwKnJ16lGLpjY+Pf7c8c3nre9b9dTNGrBJN0UXdDw6n7nafUuvXILEIAkKvwCAAAAnVZDPKaXZ3zceDX8zq+c13jCbuziGXph2gjVxOrUEI/pgk8e1W/euVkHDL5cny6cogs/faxJ5aXUW4s9m6FKWGczY/mCxnW7IqXi55WfP6OdXzmvSbKvFK5Cv33c6y0m+yY8VYLKBFg11CRV0T7mnRtVE6vXtOXzm1RRS3bZZ0/pnTljdfWXz+vspGolE5fO1ldLZjarENwelV0/mjehyfN8k/pSK8C2FPOfPrin2bBlDdkrp3RNc2I4OdEpeXxtvF7Tl89v1r41UpelT3X3vKeRsuf8Nrt1eBo1sdyrSRTCwAlDmjx/acbH6Ru2oBDbb2rSXE1K5fp/ffFc3n1WZHlBSllFp9zEPN7k80shtfXHz3JR7KozWPUkKrpXFeHigeR3jclI+C2S1L1TLsfp1irmMWdsUgW8dNbt3rdo8wYAAB3HV0tm6rrRL6Y9L3FDUpLlJwsmNxufeoFkS0lrybZcbb2s47N9r2+NH6/3/SbP23o+o60XdjXE2//zfbqL7TuC1G130rL2TSYvBw3xmD6aN0FL69NfhCqpWbKvJF346WN5z+uJKe9p8KzPdc+EwXppxsfa87WLdO6o/2rCklkZp5lbu6RJBe+XW3keMlfJFxdILZy3zZbwmzKqPo8Kv1Lu3+PzqRycXKThd+/d3pjsK0kHDi7fpH2UN3fvtOdAqfALAAAAdFL/+vI5PZ+SmHvhJ49pwpKZmrhsjiTpss+f0jEb76HXvvm02fQ3jnlJd+7yB0nNb92z+Wrrtjj/QzfYsbWht7vEFc1za5fo4Df/2Th88I8uUq+oGuO7c8Y13k7rmb3O0gY91tDhw1ZWdjzq7ev1v33OkSStaKjTs9M+TDuvpQ01evWbT3KO7aovn9MRG+2S7yJ1Wu6uX759vSZHJ1DfP/CKnK5WXxWtiNWpW2WXZsOHzPoybftJ0ftekobODm0+XTBZv3//riYJJadv+RN9smByY5uW1uHCuuXqVlndpPJprk798N68p2nCUm911pB3HDuuualGp5xsTda1slrLUxI9qyqSKvxWVktJlRN/PuwaSdKg/S/Uajkk6WaSeqI2l997Uk/WtvQjTy4/ItXE69Wn5Vm3Wm2sXn8f+aBmrlioK7c/WreNf63J+HW6tW7uMY+rKo9b1GXqI1nih5rpy+frySnv6YXpI/LuM9veKFZGJy9jHtffRzyg2niDbvvBSe22H524dLZO+eAeza9bWsS5dIyE3/oy2l5QHhLv08o27vsSnwka4jFVVYS+ko8XpqYXRHSUJPpykHpcLuaaPX74rS03KpLkKlYAAKBzS77Ac9iPL2k8p/Px/In64wd3S5IenfxOswqv3+q6mmasWJCx313W3FyDZn7eqphSL/4t9gXxqecC2npBXVvvGpFPgl+r+o/H9Njk4aqNN+jX/fZUt8pq/e6924s6z2KIebzFOzXVxZr+pvKtrqvl3H/ivfHOAZeqOocqteXI3ZvcmW34AZc1fscshs16raMbxrzUZNigmZ/r4/kT9cp+5zdr7+5NfguSpLW7tuIOa3k4ZIMdNfDrIUkxJMWT8t7Nti9oXuE3v/dtfdL3/Wxa2h9Vt/H8A5DN0oYa7ffGpZKktbr21ov7/l+JI2pfq+aeHwAAAECb/G/aiGbJvpL0xszPmg17ZPI7afv4TlJSb2rSWG0OFR9fmD5CF37viBbbFdvShho9Mfk9HbbhTjrozfyuJN7vjUsbTygnkn2lkNCXeqJ5ds2ixr/fmzc+Y597r7NNXgm/v9ts35zbQvpo/teNyb6StMur5zdJWI17XBVFqHxXLNl+VMiU7LVxz2/l3P+jk4c3O0GYfPW9JF362VO6+PtHpp1++JyxOnvkQ+rdpYfu2/VPWq/76s3ajJj3tU758B71re6hl/Y9t6AndlNPZjbEY1KeCb8tnRBNd1J9xPyVtyvLdHJ//0GXaave62vtbn30/tyvVBuv17X9j9Mu39o84+3kGuKxJifCk8XU8g8+qdX6QhJW5kTNXH4CqkmpXl5oT055Tx/M+0qSdPbHDzcb39rtJeZxVamwCb+JY9/5nzzapAp+frJV+C1+Aqe769QP79WI+eEWgldsd3STakKPT35X14x+ock0u7x6frvcPrPQlXwz6QipiXNqFuutOWNKHQbakbvrjZmfafvV+2mtbr3THrsSe5fki1JaNy/pzBH36505Y9OOj8ubfJaiwm/hpH4mGzn/a/1o3e+1qc9E4nbqbZLTaa/9cN8uPdtlPgCAjsnddcw7N2rNrqvplp1+12Ev8u4MTk+qBilJJ793hx7a4y+S1Jj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            "text/plain": [
              "<Figure size 1440x316.8 with 4 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 283,
              "width": 1406
            },
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_components.plot(\n",
        "    components=[\n",
        "        plot_components.TranscriptAnnotation(transcripts),\n",
        "        plot_components.Tracks(output.rna_seq),\n",
        "    ],\n",
        "    interval=output.rna_seq.interval,\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBtGbT0skYVu"
      },
      "source": [
        "This plot visualises the 3 predicted RNA-seq tracks and also marks the location\n",
        "of the MANE select transcript per gene in the 1MB region.\n",
        "\n",
        "We can zoom in to the middle of the plot by resizing the interval:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "height": 278
        },
        "executionInfo": {
          "elapsed": 852,
          "status": "ok",
          "timestamp": 1761827046537,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "J8_0AvHbkYVu",
        "outputId": "a3661b2c-79e6-477e-9160-56a91668f988"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 1406
            },
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_components.plot(\n",
        "    components=[\n",
        "        plot_components.TranscriptAnnotation(\n",
        "            transcripts, fig_height=0.1\n",
        "        ),\n",
        "        plot_components.Tracks(output.rna_seq),\n",
        "    ],\n",
        "    interval=output.rna_seq.interval.resize(2**15),\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1o5l2yUGkYVu"
      },
      "source": [
        "You can see here that predicted RNA-seq values are nicely aligned with the\n",
        "location of exons, and that the predictions are stranded – the predicted values\n",
        "are much higher for the positive strand, where the gene is located. We see that\n",
        "the *CYP2B6* gene is on the positive strand since the arrows in the transcript\n",
        "go from left to right.\n",
        "\n",
        "For more detail on the visualization library, please refer to the\n",
        "[visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html)\n",
        "and\n",
        "[library documentation](https://www.alphagenomedocs.com/api/visualization.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rPQJQirLkYVu"
      },
      "source": [
        "## Predict variant effects"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktZ0PqhQkYVu"
      },
      "source": [
        "We can predict the effect of a variant on a specific output type and tissue by\n",
        "making predictions for the reference (REF) and alternative (ALT) allele\n",
        "sequences.\n",
        "\n",
        "We specify the variant by defining a `genome.Variant` object. The specific\n",
        "variant below is a known variant affecting gene expression in colon tissue:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "YaxavVV_kYVu",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827046941,
          "user_tz": 0,
          "elapsed": 55,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "variant = genome.Variant(\n",
        "    chromosome='chr22',\n",
        "    position=36201698,\n",
        "    reference_bases='A',  # Can differ from the true reference genome base.\n",
        "    alternate_bases='C',\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HE2_iU6akYVu"
      },
      "source": [
        "Next, we define the interval over which to make the REF and ALT predictions. A\n",
        "quick way to get a `genome.Interval` from a `genome.Variant` is by calling\n",
        "`.reference_interval`, which we can resize to a model-compatible sequence\n",
        "length:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "0jxThai2kYVu",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827047208,
          "user_tz": 0,
          "elapsed": 2,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "interval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oupoz0bGkYVu"
      },
      "source": [
        "We then use `predict_variant` to get the REF and ALT RNA-seq predictions in the\n",
        "interval for \"Colon - Transverse\" tissue (`UBERON:0001157`):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "TZbfcADGkYVu",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827049006,
          "user_tz": 0,
          "elapsed": 1556,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "variant_output = dna_model.predict_variant(\n",
        "    interval=interval,\n",
        "    variant=variant,\n",
        "    requested_outputs=[dna_client.OutputType.RNA_SEQ],\n",
        "    ontology_terms=['UBERON:0001157'],\n",
        ")  # Colon - Transverse."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzYgHwSAkYVu"
      },
      "source": [
        "We can plot the predicted REF and ALT values as a single plot and zoom in on the\n",
        "affected gene to better visualise the variant's effect on gene expression:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "height": 333
        },
        "executionInfo": {
          "elapsed": 1135,
          "status": "ok",
          "timestamp": 1761827050414,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "_OTUiWNjkYVu",
        "outputId": "fd15c3b2-7482-434b-d224-535d2c2ab1e7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 5 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 316,
              "width": 1472
            },
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "transcripts = transcript_extractor.extract(interval)\n",
        "\n",
        "plot_components.plot(\n",
        "    [\n",
        "        plot_components.TranscriptAnnotation(transcripts),\n",
        "        plot_components.OverlaidTracks(\n",
        "            tdata={\n",
        "                'REF': variant_output.reference.rna_seq,\n",
        "                'ALT': variant_output.alternate.rna_seq,\n",
        "            },\n",
        "            colors={'REF': 'dimgrey', 'ALT': 'red'},\n",
        "        ),\n",
        "    ],\n",
        "    interval=variant_output.reference.rna_seq.interval.resize(2**15),\n",
        "    # Annotate the location of the variant as a vertical line.\n",
        "    annotations=[plot_components.VariantAnnotation([variant], alpha=0.8)],\n",
        ")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSz201LRkYVu"
      },
      "source": [
        "We see that the ALT allele (base 'C' at position 36201698) is associated with\n",
        "both lower expression and an exon skipping event in the *APOL4* gene on the\n",
        "negative strand. Note that we can ignore the uppermost line plot which shows a\n",
        "very minimal predicted amount of expression on the positive DNA strand (check\n",
        "the y axis scales). It is possible to adjust the y axes limits, see\n",
        "[visualization basics](https://www.alphagenomedocs.com/visualization_library_basics.html#visualization-library-basics)\n",
        "and\n",
        "[library documentation](https://www.alphagenomedocs.com/api/visualization.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7s4bDxczkYVu"
      },
      "source": [
        "## Scoring the effect of a genetic variant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fTtdkpM-Ycm"
      },
      "source": [
        "Scoring the effect of a genetic variant involves making predictions for the REF\n",
        "and ALT sequences and aggregating the track signal. This is implemented in\n",
        "`score_variant`, which uses specific `variant_scorer` configs for aggregation.\n",
        "\n",
        "We provide a set of recommended variant scoring configurations as a dictionary\n",
        "(`variant_scorers.RECOMMENDED_VARIANT_SCORERS`), covering all output types,\n",
        "which we have assessed for their performance at domain-specific tasks. See the\n",
        "[variant scoring documentation](https://www.alphagenomedocs.com/variant_scoring.html)\n",
        "for more information. Here is a quick demo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Aj_T3sE8kYVu",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827052238,
          "user_tz": 0,
          "elapsed": 1519,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "variant_scorer = variant_scorers.RECOMMENDED_VARIANT_SCORERS['RNA_SEQ']\n",
        "\n",
        "variant_scores = dna_model.score_variant(\n",
        "    interval=interval, variant=variant, variant_scorers=[variant_scorer]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncUCGp0JkYVu"
      },
      "source": [
        "The returned `variant_scores` is a list of length 1 because we only specified 1\n",
        "scorer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "executionInfo": {
          "elapsed": 54,
          "status": "ok",
          "timestamp": 1761827052546,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "xI2aR0OtkYVu",
        "outputId": "94d58f09-cf6e-4c45-d45c-423256d6e00b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "len(variant_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oow5vOLVkYVu"
      },
      "source": [
        "The actual scores per variant are in `AnnData` format, which is a way of\n",
        "annotating data (the numerical scores) with additional information about the\n",
        "rows and columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1761827052869,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "WCy_9aQ2kYVu",
        "outputId": "00c1cdfa-9064-411d-e9a2-d7ec4cf41517"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AnnData object with n_obs × n_vars = 37 × 667\n",
              "    obs: 'gene_id', 'strand', 'gene_name', 'gene_type'\n",
              "    var: 'name', 'strand', 'Assay title', 'ontology_curie', 'biosample_name', 'biosample_type', 'biosample_life_stage', 'gtex_tissue', 'data_source', 'endedness', 'genetically_modified', 'nonzero_mean'\n",
              "    uns: 'interval', 'variant', 'variant_scorer'\n",
              "    layers: 'quantiles'"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "variant_scores = variant_scores[0]\n",
        "variant_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2URPdQGtkYVu"
      },
      "source": [
        "`AnnData` objects have the following components:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssPveqJvkYVu"
      },
      "source": [
        "<a href=\"https://services.google.com/fh/files/misc/anndata.png\"><img src=\"https://services.google.com/fh/files/misc/anndata.png\" alt=\"anndata\" border=\"0\" height=500></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1u94SdOkYVu"
      },
      "source": [
        "We have a variant effect score for each of the 37 genes in the interval and each\n",
        "of the 667 `RNA_SEQ` tracks:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1761827053213,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "eyv_SHT_kYVu",
        "outputId": "2362e98a-ada7-4ae3-bd95-609daccce1e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(37, 667)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "variant_scores.X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3cT3a6FkYVu"
      },
      "source": [
        "We can access information on the 37 genes using `.obs`. Here are just first 5\n",
        "genes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "height": 206
        },
        "executionInfo": {
          "elapsed": 54,
          "status": "ok",
          "timestamp": 1761827053525,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "AQBAsm2skYVu",
        "outputId": "d5083d46-1e13-4635-c082-cc78ab13b539"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"variant_scores\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"gene_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"ENSG00000100336.18\",\n          \"ENSG00000100348.10\",\n          \"ENSG00000100342.22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"strand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"+\",\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gene_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"APOL4\",\n          \"TXN2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gene_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"protein_coding\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
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            ],
            "text/plain": [
              "              gene_id strand gene_name       gene_type\n",
              "0  ENSG00000100320.24      -    RBFOX2  protein_coding\n",
              "1  ENSG00000100336.18      -     APOL4  protein_coding\n",
              "2  ENSG00000100342.22      +     APOL1  protein_coding\n",
              "3  ENSG00000100345.23      -      MYH9  protein_coding\n",
              "4  ENSG00000100348.10      -      TXN2  protein_coding"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "variant_scores.obs.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6mVhremkYVu"
      },
      "source": [
        "Note that if you are using a variant scorer that is not gene-specific (i.e., a\n",
        "`variant_scorers.CenterMaskScorer`), then `variant_scores.X` would have shape\n",
        "`(1, 667)` and there will be no gene metadata available since there is no\n",
        "concept of genes in this scenario.\n",
        "\n",
        "The description of each track is accessed using `.var` (this is the same\n",
        "dataframe as the output metadata, but is included alongside the variant scores\n",
        "for convenience):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "height": 930
        },
        "executionInfo": {
          "elapsed": 107,
          "status": "ok",
          "timestamp": 1761827053927,
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        },
        "id": "J_VRdJMakYVu",
        "outputId": "77abb536-1b13-4dd5-c517-b8386c1a81dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>name</th>\n",
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              "      <th>genetically_modified</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CL:0000047 polyA plus RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:0000047</td>\n",
              "      <td>neuronal stem cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
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              "      <td>0.143617</td>\n",
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              "      <th>1</th>\n",
              "      <td>CL:0000062 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000062</td>\n",
              "      <td>osteoblast</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.094144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CL:0000084 polyA plus RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:0000084</td>\n",
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              "      <td>encode</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.124296</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CL:0000084 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000084</td>\n",
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              "      <td>primary_cell</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.100934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CL:0000115 total RNA-seq</td>\n",
              "      <td>+</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000115</td>\n",
              "      <td>endothelial cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td>adult</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.135553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>UBERON:0018115 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018115</td>\n",
              "      <td>left renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.268222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>663</th>\n",
              "      <td>UBERON:0018116 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018116</td>\n",
              "      <td>right renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.258522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>664</th>\n",
              "      <td>UBERON:0018117 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018117</td>\n",
              "      <td>left renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.215190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>665</th>\n",
              "      <td>UBERON:0018118 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018118</td>\n",
              "      <td>right renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td>embryonic</td>\n",
              "      <td></td>\n",
              "      <td>encode</td>\n",
              "      <td>single</td>\n",
              "      <td>False</td>\n",
              "      <td>0.365676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>666</th>\n",
              "      <td>UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0036149</td>\n",
              "      <td>suprapubic skin</td>\n",
              "      <td>tissue</td>\n",
              "      <td>adult</td>\n",
              "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
              "      <td>gtex</td>\n",
              "      <td>paired</td>\n",
              "      <td>False</td>\n",
              "      <td>0.045404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>667 rows × 12 columns</p>\n",
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            ],
            "text/plain": [
              "                                                  name  ... nonzero_mean\n",
              "0                        CL:0000047 polyA plus RNA-seq  ...     0.143617\n",
              "1                             CL:0000062 total RNA-seq  ...     0.094144\n",
              "2                        CL:0000084 polyA plus RNA-seq  ...     0.124296\n",
              "3                             CL:0000084 total RNA-seq  ...     0.100934\n",
              "4                             CL:0000115 total RNA-seq  ...     0.135553\n",
              "..                                                 ...  ...          ...\n",
              "662                  UBERON:0018115 polyA plus RNA-seq  ...     0.268222\n",
              "663                  UBERON:0018116 polyA plus RNA-seq  ...     0.258522\n",
              "664                  UBERON:0018117 polyA plus RNA-seq  ...     0.215190\n",
              "665                  UBERON:0018118 polyA plus RNA-seq  ...     0.365676\n",
              "666  UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...  ...     0.045404\n",
              "\n",
              "[667 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "variant_scores.var"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrbvEe4xkYVu"
      },
      "source": [
        "Some handy additional metadata can be found in `.uns`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "executionInfo": {
          "elapsed": 54,
          "status": "ok",
          "timestamp": 1761827054262,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "n3GGvosdkYVu",
        "outputId": "3d57d678-5a55-4a1c-bdd1-15cf6f334c00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Interval: chr22:35677410-36725986:.\n",
            "Variant: chr22:36201698:A>C\n",
            "Variant scorer: GeneMaskLFCScorer(requested_output=RNA_SEQ)\n"
          ]
        }
      ],
      "source": [
        "print(f'Interval: {variant_scores.uns[\"interval\"]}')\n",
        "print(f'Variant: {variant_scores.uns[\"variant\"]}')\n",
        "print(f'Variant scorer: {variant_scores.uns[\"variant_scorer\"]}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0WK-6IpkYVu"
      },
      "source": [
        "We recommend interacting with variant scores by flattening `AnnData` objects\n",
        "using `tidy_scores`, which produces a dataframe with each row being a single\n",
        "score for each combination of (variant, gene, scorer, ontology). It optionally\n",
        "excludes stranded tracks which do not match the gene’s strand for gene-specific\n",
        "scorer.\n",
        "\n",
        "The `raw_score` column contains the same values as stored in `variant_scores.X`.\n",
        "The `quantile_score` column is the rank of the `raw_score` in the distribution\n",
        "of scores for a background set of common variants, represented as a quantile\n",
        "probability. This allows for direct comparison across variant scoring strategies\n",
        "that yield scores on different scales. See\n",
        "[FAQs](https://www.alphagenomedocs.com/faqs.html#what-is-the-difference-between-a-quantile-score-and-raw-score)\n",
        "for further details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "height": 930
        },
        "executionInfo": {
          "elapsed": 643,
          "status": "ok",
          "timestamp": 1761827055176,
          "user": {
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            "userId": ""
          },
          "user_tz": 0
        },
        "id": "cVG5ApfkkYVu",
        "outputId": "8d59a35b-6953-4964-9bea-51bb37da6249"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "repr_error": "Out of range float values are not JSON compliant: nan",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variant_id</th>\n",
              "      <th>scored_interval</th>\n",
              "      <th>gene_id</th>\n",
              "      <th>gene_name</th>\n",
              "      <th>gene_type</th>\n",
              "      <th>gene_strand</th>\n",
              "      <th>junction_Start</th>\n",
              "      <th>junction_End</th>\n",
              "      <th>output_type</th>\n",
              "      <th>variant_scorer</th>\n",
              "      <th>track_name</th>\n",
              "      <th>track_strand</th>\n",
              "      <th>Assay title</th>\n",
              "      <th>ontology_curie</th>\n",
              "      <th>biosample_name</th>\n",
              "      <th>biosample_type</th>\n",
              "      <th>gtex_tissue</th>\n",
              "      <th>raw_score</th>\n",
              "      <th>quantile_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000100320</td>\n",
              "      <td>RBFOX2</td>\n",
              "      <td>protein_coding</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>CL:0000047 polyA plus RNA-seq</td>\n",
              "      <td>-</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:0000047</td>\n",
              "      <td>neuronal stem cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td></td>\n",
              "      <td>0.001068</td>\n",
              "      <td>6.486191e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000100320</td>\n",
              "      <td>RBFOX2</td>\n",
              "      <td>protein_coding</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>CL:0000062 total RNA-seq</td>\n",
              "      <td>-</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000062</td>\n",
              "      <td>osteoblast</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td></td>\n",
              "      <td>-0.000143</td>\n",
              "      <td>-3.431121e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000100320</td>\n",
              "      <td>RBFOX2</td>\n",
              "      <td>protein_coding</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>CL:0000084 polyA plus RNA-seq</td>\n",
              "      <td>-</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>CL:0000084</td>\n",
              "      <td>T-cell</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td></td>\n",
              "      <td>-0.000452</td>\n",
              "      <td>-4.312567e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000100320</td>\n",
              "      <td>RBFOX2</td>\n",
              "      <td>protein_coding</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>CL:0000084 total RNA-seq</td>\n",
              "      <td>-</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000084</td>\n",
              "      <td>T-cell</td>\n",
              "      <td>primary_cell</td>\n",
              "      <td></td>\n",
              "      <td>-0.001039</td>\n",
              "      <td>-6.418862e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000100320</td>\n",
              "      <td>RBFOX2</td>\n",
              "      <td>protein_coding</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>CL:0000115 total RNA-seq</td>\n",
              "      <td>-</td>\n",
              "      <td>total RNA-seq</td>\n",
              "      <td>CL:0000115</td>\n",
              "      <td>endothelial cell</td>\n",
              "      <td>in_vitro_differentiated_cells</td>\n",
              "      <td></td>\n",
              "      <td>0.000349</td>\n",
              "      <td>3.831612e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14647</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>processed_pseudogene</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>UBERON:0018115 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018115</td>\n",
              "      <td>left renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td></td>\n",
              "      <td>0.002708</td>\n",
              "      <td>8.917613e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14648</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>processed_pseudogene</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>UBERON:0018116 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018116</td>\n",
              "      <td>right renal pelvis</td>\n",
              "      <td>tissue</td>\n",
              "      <td></td>\n",
              "      <td>0.007722</td>\n",
              "      <td>9.939092e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14649</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>processed_pseudogene</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>UBERON:0018117 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018117</td>\n",
              "      <td>left renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td></td>\n",
              "      <td>0.005573</td>\n",
              "      <td>9.824973e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14650</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>processed_pseudogene</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>UBERON:0018118 polyA plus RNA-seq</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0018118</td>\n",
              "      <td>right renal cortex interstitium</td>\n",
              "      <td>tissue</td>\n",
              "      <td></td>\n",
              "      <td>0.004530</td>\n",
              "      <td>9.645371e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14651</th>\n",
              "      <td>chr22:36201698:A&gt;C</td>\n",
              "      <td>chr22:35677410-36725986:.</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>ENSG00000293594</td>\n",
              "      <td>processed_pseudogene</td>\n",
              "      <td>-</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>RNA_SEQ</td>\n",
              "      <td>GeneMaskLFCScorer(requested_output=RNA_SEQ)</td>\n",
              "      <td>UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...</td>\n",
              "      <td>.</td>\n",
              "      <td>polyA plus RNA-seq</td>\n",
              "      <td>UBERON:0036149</td>\n",
              "      <td>suprapubic skin</td>\n",
              "      <td>tissue</td>\n",
              "      <td>Skin_Not_Sun_Exposed_Suprapubic</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.137757e-12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14652 rows × 19 columns</p>\n",
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            "text/plain": [
              "               variant_id            scored_interval  ... raw_score quantile_score\n",
              "0      chr22:36201698:A>C  chr22:35677410-36725986:.  ...  0.001068   6.486191e-01\n",
              "1      chr22:36201698:A>C  chr22:35677410-36725986:.  ... -0.000143  -3.431121e-01\n",
              "2      chr22:36201698:A>C  chr22:35677410-36725986:.  ... -0.000452  -4.312567e-01\n",
              "3      chr22:36201698:A>C  chr22:35677410-36725986:.  ... -0.001039  -6.418862e-01\n",
              "4      chr22:36201698:A>C  chr22:35677410-36725986:.  ...  0.000349   3.831612e-01\n",
              "...                   ...                        ...  ...       ...            ...\n",
              "14647  chr22:36201698:A>C  chr22:35677410-36725986:.  ...  0.002708   8.917613e-01\n",
              "14648  chr22:36201698:A>C  chr22:35677410-36725986:.  ...  0.007722   9.939092e-01\n",
              "14649  chr22:36201698:A>C  chr22:35677410-36725986:.  ...  0.005573   9.824973e-01\n",
              "14650  chr22:36201698:A>C  chr22:35677410-36725986:.  ...  0.004530   9.645371e-01\n",
              "14651  chr22:36201698:A>C  chr22:35677410-36725986:.  ...  0.000000   1.137757e-12\n",
              "\n",
              "[14652 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "\n",
        "variant_scorers.tidy_scores([variant_scores], match_gene_strand=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "055sHwjykYVu"
      },
      "source": [
        "## Highlighting important regions with *in silico* mutagenesis\n",
        "\n",
        "To highlight which regions in a DNA sequence are functionally important for a\n",
        "final variant prediction, we can perform an **in silico mutagenesis** (ISM)\n",
        "analysis by scoring all possible single nucleotide variants in a specific\n",
        "interval.\n",
        "\n",
        "Here is a visual overview of this process:\n",
        "\n",
        "<a href=\"https://services.google.com/fh/files/misc/ism_green_v2.png\"><img src=\"https://services.google.com/fh/files/misc/ism_green_v2.png\" alt=\"ISM\" border=\"0\" height=500></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O93kx8lckYVu"
      },
      "source": [
        "We define an `ism_interval`, which is a relatively small region of DNA that we\n",
        "want to systematically mutate. We also define the `sequence_interval`, which is\n",
        "the contextual interval the model will use when making predictions for each\n",
        "variant."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "9Xhk_TsHkYVu",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827242411,
          "user_tz": 0,
          "elapsed": 2,
          "user": {
            "displayName": "",
            "userId": ""
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        }
      },
      "outputs": [],
      "source": [
        "# 16KB DNA sequence to use as context when making predictions.\n",
        "sequence_interval = genome.Interval('chr20', 3_753_000, 3_753_400)\n",
        "sequence_interval = sequence_interval.resize(dna_client.SEQUENCE_LENGTH_16KB)\n",
        "\n",
        "# Mutate all bases in the central 256-base region of the sequence_interval.\n",
        "ism_interval = sequence_interval.resize(256)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9LSDA0QkYVu"
      },
      "source": [
        "Next, we define the scorer we want to use to score each of the ISM variants.\n",
        "Here, we use a center mask scorer on predicted `DNASE` values, which will score\n",
        "each variant's effect on DNA accessibility in the 500bp vicinity. See the\n",
        "[variant scoring documentation](https://www.alphagenomedocs.com/variant_scoring.html)\n",
        "for more information on variant scoring."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "twYn3L9qkYVu",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827242735,
          "user_tz": 0,
          "elapsed": 53,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "dnase_variant_scorer = variant_scorers.CenterMaskScorer(\n",
        "    requested_output=dna_client.OutputType.DNASE,\n",
        "    width=501,\n",
        "    aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbQxCvutkYVu"
      },
      "source": [
        "Finally, we can use `score_variants` (notice the plural s) to score all\n",
        "variants.\n",
        "\n",
        "Note that this operation is quite expensive. For speed reasons, we recommend\n",
        "using shorter input sequences for the contextual `sequence_interval` and\n",
        "narrower `ism_interval` regions to mutate if possible."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "height": 49,
          "referenced_widgets": [
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        "executionInfo": {
          "elapsed": 15045,
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          "timestamp": 1761827258044,
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e45068b1a4c447dda264e040826f87e0",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "  0%|          | 0/26 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "variant_scores = dna_model.score_ism_variants(\n",
        "    interval=sequence_interval,\n",
        "    ism_interval=ism_interval,\n",
        "    variant_scorers=[dnase_variant_scorer],\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3iJ9E-ckYVu"
      },
      "source": [
        "The length of the returned `variant_scores` is 768, since we scored 768 variants\n",
        "(256 positions * 3 alternative bases per position):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "executionInfo": {
          "elapsed": 2,
          "status": "ok",
          "timestamp": 1761827258297,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "NrRgRYF1kYVu",
        "outputId": "6bdf9cc9-df9a-4f0c-d4f9-80c74bcc1f31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "768"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "len(variant_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_e2WoG6pkYVu"
      },
      "source": [
        "Each variant has scores of shape `(1, 305)`, reflecting the fact that we are not\n",
        "using a gene-centric scorer and that there are 305 `DNASE` tracks:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1761827258614,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "f-raKWmbkYVu",
        "outputId": "a381ccd7-28e7-4955-f767-a83c16ff0af2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 305)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "# Index into first variant and first scorer.\n",
        "variant_scores[0][0].X.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyDoTBdAkYVu"
      },
      "source": [
        "To understand which positions are most influential in the predictions, we can\n",
        "visualize these scores using a sequence logo. This requires summarizing the\n",
        "scores into a single scalar value per variant.\n",
        "\n",
        "As an example, let's extract the DNASE score for just the K562 cell line, a\n",
        "widely used experimental model. Alternatively, you could average across multiple\n",
        "tissues to obtain a single scalar value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "jK9UF4PZkYVu",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827259093,
          "user_tz": 0,
          "elapsed": 203,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "def extract_k562(adata):\n",
        "  values = adata.X[:, adata.var['ontology_curie'] == 'EFO:0002067']\n",
        "  assert values.size == 1\n",
        "  return values.flatten()[0]\n",
        "\n",
        "\n",
        "ism_result = ism.ism_matrix(\n",
        "    [extract_k562(x[0]) for x in variant_scores],\n",
        "    variants=[v[0].uns['variant'] for v in variant_scores],\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYtOmG4xkYVu"
      },
      "source": [
        "The shape of `ism_result` is `(256, 4`) since we have 1 score per position per\n",
        "each of the 4 DNA bases.\n",
        "\n",
        "Note that in this case, our call to `ism.ism_matrix()` had the argument\n",
        "`multiply_by_sequence` set to 'True', so the output array contains non-zero\n",
        "values only for the bases corresponding to the reference sequence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "executionInfo": {
          "elapsed": 53,
          "status": "ok",
          "timestamp": 1761827259381,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "ONduc-kwkYVv",
        "outputId": "09aff1bd-abad-44b8-8b08-d431e987ce8c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(256, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "ism_result.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wj0VMFwCkYVv"
      },
      "source": [
        "Finally, we plot the contribution scores as a sequence logo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "height": 115
        },
        "executionInfo": {
          "elapsed": 4882,
          "status": "ok",
          "timestamp": 1761827264495,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "0X9fwKw6kYVv",
        "outputId": "7f4c8598-1382-4a16-bfea-abe92fbe33cd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2520x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 98,
              "width": 2078
            },
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_components.plot(\n",
        "    [\n",
        "        plot_components.SeqLogo(\n",
        "            scores=ism_result,\n",
        "            scores_interval=ism_interval,\n",
        "            ylabel='ISM K562 DNase',\n",
        "        )\n",
        "    ],\n",
        "    interval=ism_interval,\n",
        "    fig_width=35,\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qkuuj1p7kYVv"
      },
      "source": [
        "This plot shows that the sequence between positions ~225 to ~240 has the\n",
        "strongest effect on predicted nearby DNAse in K562 cells.\n",
        "\n",
        "These contribution scores can be used to systematically discover motifs\n",
        "important for different modalities and cell types, find the transcription\n",
        "factors binding those motifs and map motif instances across the genome. Here are\n",
        "a few tools you can use to do this: -\n",
        "[tfmodisco-lite](https://github.com/jmschrei/tfmodisco-lite/) -\n",
        "[tangermeme](https://github.com/jmschrei/tangermeme) -\n",
        "[tomtom](https://meme-suite.org/meme/tools/tomtom)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6Vum-X6ILaz"
      },
      "source": [
        "## Making mouse predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-E-kEitg6ru"
      },
      "source": [
        "So far, this notebook has focused on predictions for human\n",
        "(`Organism.HOMO_SAPIENS`). To generate predictions for mouse, specify the\n",
        "organism as `Organism.MUS_MUSCULUS` instead. Please note that the supported\n",
        "ontology terms differ between species.\n",
        "\n",
        "The following example demonstrates how to call `predict_sequence` for mouse\n",
        "predictions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "QjFVRG1QLVba",
        "language": "python",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1761827265900,
          "user_tz": 0,
          "elapsed": 1099,
          "user": {
            "displayName": "",
            "userId": ""
          }
        }
      },
      "outputs": [],
      "source": [
        "output = dna_model.predict_sequence(\n",
        "    sequence='GATTACA'.center(\n",
        "        dna_client.SEQUENCE_LENGTH_1MB, 'N'\n",
        "    ),  # Pad to valid sequence length.\n",
        "    organism=dna_client.Organism.MUS_MUSCULUS,\n",
        "    requested_outputs=[dna_client.OutputType.DNASE],\n",
        "    ontology_terms=['UBERON:0002048'],  # Lung.\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwpupBP1hPgh"
      },
      "source": [
        "And here is an example of calling `predict_interval` for a mouse genomic\n",
        "interval:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "executionInfo": {
          "elapsed": 395,
          "status": "ok",
          "timestamp": 1761827266550,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": 0
        },
        "id": "lKOIZAtMLrEu",
        "outputId": "7a30905d-5994-43ee-bf48-24d854a0e1b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1048576, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "interval = genome.Interval('chr1', 3_000_000, 3_000_001).resize(\n",
        "    dna_client.SEQUENCE_LENGTH_1MB\n",
        ")\n",
        "\n",
        "output = dna_model.predict_interval(\n",
        "    interval=interval,\n",
        "    organism=dna_client.Organism.MUS_MUSCULUS,\n",
        "    requested_outputs=[dna_client.OutputType.RNA_SEQ],\n",
        "    ontology_terms=['UBERON:0002048'],  # Lung.\n",
        ")\n",
        "\n",
        "output.rna_seq.values.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1isDYNjkYVv"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "That's it for the quick start guide. To dive in further, check out our\n",
        "[other tutorials](https://www.alphagenomedocs.com/tutorials/index.html)."
      ]
    }
  ],
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